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About This Role
Overview:
QXO is a leading distributor and installer of building products serving an $800 billion market. The company’s mission is to modernize the building products industry through advanced technology and a best-in-class customer experience. QXO is North America’s largest distributor and installer of insulation, the second-largest distributor of roofing products, the second-largest publicly traded distributor of lumber and building materials, and the largest distributor of waterproofing products. The company is targeting $50 billion in annual revenue within the decade through accretive acquisitions and organic growth. For more information, visit QXO.com.
What you will do::
We are seeking a highly skilled Senior Software Engineer, AI to design, build, and deploy production-grade AI agents that augment workflows across our organization. The ideal candidate has hands-on experience with agentic frameworks, MCP servers, LLM orchestration libraries, and robust testing/monitoring practices. This role blends software engineering excellence with applied machine learning and offers the opportunity to push the boundaries of intelligent automation in real business environments. Responsibilities
AI Agent Development & Deployment
- Architect, build, and optimize AI agents using modern agent frameworks (e.g., LangChain, LlamaIndex, OpenAI/MCP-based ecosystems, or equivalents).
- Implement MCP (Model Context Protocol) servers, custom tools, and integrations to enable secure and scalable agent capabilities.
- Design agentic workflows that can operate autonomously, perform multi-step reasoning, and interact with structured/unstructured data sources.
- Package and deploy agents into production environments with attention to reliability, observability, and performance.
Sales & Marketing Agent Use Cases
- Build agents that support Sales Representatives, such as:
- Lead and account research, enrichment, and prioritization.
- Drafting and personalizing outbound emails and sequences.
- Summarizing calls, meetings, and account activity to drive next-best actions.
- Integrating with CRM and sales tools (e.g., Salesforce, HubSpot, Outreach) to automate data entry and insight surfacing.
- Generating detailed bills of materials (BOMs), estimates, and quotes from drawings, specs, takeoffs, or CRM/opportunity data, including price checks, margin validation, and versioning.
- Build agents that support Marketing, such as:
- Generating and localizing content for campaigns, landing pages, and nurture programs.
- Assisting with audience segmentation, experimentation, and performance analysis.
- Powering internal “marketing copilots” that answer questions from campaign, web, and analytics data.
- Partner with the business to identify high-ROI workflows for automation and to measure the impact of deployed agents on pipeline, conversion, quoting speed/accuracy, and engagement metrics.
Systems Engineering & Tooling
- Develop internal libraries, reusable modules, and standardized patterns for building agentic applications.
- Integrate agent systems with enterprise APIs, cloud services, databases, pricing/catalog systems, and operational infrastructure.
- Build CI/CD pipelines for both model-related code and agent-specific behavior, including automated testing, evaluation harnesses, and regression detection for LLM-powered systems.
Testing, Evaluation & Monitoring
- Create frameworks for continuous evaluation of agents, including prompt tests, scenario simulations, and safety/robustness checks.
- Monitor agent performance in production, diagnose failures, and iterate quickly on improvements.
- Implement logging, analytics, and feedback loops to guide ongoing training or refinement—especially for critical revenue and quoting workflows.
Collaboration & Strategy
- Work closely with product, engineering, Sales, Marketing, and domain experts to translate business processes into agentic flows.
- Partner with stakeholders to identify automation opportunities and design AI-powered operational solutions.
- Contribute to internal documentation, best practices, and AI engineering guidelines.
What you will bring:: Required
- 3–7+ years of experience as a Software Engineer, Machine Learning Engineer, or AI Engineer (flexible based on seniority).
- Proven experience building AI agents or LLM-driven applications in production contexts.
- Hands-on work with libraries/frameworks such as LangChain, OpenAI/MCP, LlamaIndex, or similar orchestration tools.
- Proficiency with Python (or Typescript/Node) and modern development workflows.
- Experience integrating LLMs with external tools, APIs, vector databases, and retrieval systems.
- Strong understanding of CI/CD, containerization (Docker), cloud deployment (AWS/GCP/Azure), and DevOps fundamentals.
- Familiarity with automated testing approaches for LLM applications (unit tests, scenario testing, eval harnesses).
- Excellent problem-solving skills and the ability to design resilient systems in ambiguous environments.
Preferred
- Experience deploying and scaling MCP servers, custom toolchains, or enterprise agent frameworks.
- Prior work building tools or automations for Sales, Marketing, or RevOps teams (e.g., CRM-integrated apps, quoting tools, outbound tooling, marketing analytics or experimentation platforms).
- Background or project experience in the building industry (construction, materials distribution, building automation, supply chain, procurement) or other B2B industry.
- Experience with workflow engines (Airflow, Prefect, etc) and/or MLOps (Mlflow, Flyte, etc) and event-driven architectures.
- Familiarity with vector and search/retrieval systems.
- Understanding of prompt engineering, model fine-tuning, or RLHF-style evaluation frameworks.
What you will earn::
- 401(k) with employer match
- Medical, dental, and vision insurance
- PTO, company holidays, and parental leave
- Paid training and certifications
- Legal assistance and identity protection
- Pet insurance
- Employee assistance program (EAP)
QXO is an Equal Opportunity Employer. We value diversity and do not discriminate on the basis of race, color, religion, gender or sexual orientation, national origin, age, disability, or any other protected status.
To comply with Pay Transparency laws, employers must disclose an annual salary range. Actual offers depend on factors such as location, experience, skills, and market data. This position may also offer variable compensation.
Please contact [email protected] if you have any questions related to this job posting. Pay Range: USD $150,000.00 - USD $220,000.00 /Yr.
Salary Context
This $150K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At QXO, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $150K to $220K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
QXO AI Hiring
QXO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $220K - $220K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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